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##                                             python 
## "/opt/homebrew/Caskroom/miniforge/base/bin/python"

Sources: # http://amunategui.github.io/stringdist/

# installation of packages is recommended to do via the terminal (see in your console below), pip install name-of-the-required-package

# https://vincent.doba.fr/posts/20210407_install-fiona-on-windows/ & https://geopandas.org/getting_started/install.html Issues with geopandas on windows

#to install requirements type and run in the terminal:  pip install -r requirements.txt

import fiona
import geopandas as gpd 
import requests
import seaborn as sns
import matplotlib.colors as mcolors
import matplotlib.pyplot as plt
from scipy.stats import trapz
import scipy
#import nltk
import json
import tempun
#import sddk
import numpy as np
import pandas as pd
pd.options.display.max_columns = 1000  # to see all columns

1 Setup

Loading data from Sciencedata.dk

list_json <- jsonlite::fromJSON("https://sciencedata.dk/public/b6b6afdb969d378b70929e86e58ad975/EDH_text_cleaned_2021-01-21.json")
EDH <- as_tibble(list_json)

Loading data locally

# this will work only if you have the dataset available locally
EDH <- jsonlite::fromJSON("../data/large_data/EDH_text_cleaned_2021-01-21.json")
#dir.create("../figures")

1.1 Text preparation

Converting the text of inscriptions to lowercase

EDH <- EDH %>% 
  mutate(clean_text_interpretive_word_lowercase = str_to_lower(EDH$clean_text_interpretive_word)) %>% 
  mutate(clean_text_conservative_lowercase = str_to_lower(EDH$clean_text_conservative))

1.2 Preparation coordinates for mapping

# preparation of coordinates
EDH<- EDH %>% 
  separate(col = coordinates, into = c("longitude", "latitude"), sep = ",")

## Warning: Expected 2 pieces. Missing pieces filled with `NA` in 2464 rows [20,
## 34, 64, 71, 94, 149, 212, 221, 296, 311, 356, 382, 388, 390, 405, 406, 409, 412,
## 418, 421, ...].

EDH$latitude <- as.numeric(str_replace(EDH$latitude, pattern = "\\)", replacement=""))
EDH$longitude <- as.numeric(str_replace(EDH$longitude, pattern = "c\\(", replacement=""))

## Warning: NAs introduced by coercion

2 Dis Manibus epigraphic formula in space and time

Typically occurs on funerary inscriptions, invocation of the underworld gods.

Canonic Versions:

D M - only in the conservative version of the text D M S - only in the conservative version Dis Manibus Diis Manibus Dis Manibus Sacrum Diis Manibus Sacrum

2.1 Extracting inscriptions with for all typical variants of the formula

dis_manibus_regex_minimal <- "di{1,2}s manibus sacrum|di{1,2}s manibus"
dis_manibus_regex_maximal <- "\\bd m s\\b|\\bd m\\b|di{1,2}s manibus sacrum|di{1,2}s manibus|\\bdi{1,2} man[e|i]s\\b"

dis_manibus_int<- str_subset(EDH$clean_text_interpretive_word_lowercase, dis_manibus_regex_minimal)

dis_manibus_cons<- str_subset(EDH$clean_text_conservative_lowercase, dis_manibus_regex_maximal)


# separating them in a new attribute
EDH<- EDH %>% 
  mutate(form_dis_manibus_ins = str_extract(EDH$clean_text_interpretive_word_lowercase, dis_manibus_regex_minimal)) %>% 
  mutate(form_dis_manibus_cons = str_extract(EDH$clean_text_conservative_lowercase, dis_manibus_regex_maximal)) 

EDH$form_dis_manibus <- ifelse(grepl("\\w", EDH$form_dis_manibus_ins, ignore.case = T),  EDH$form_dis_manibus_ins, EDH$form_dis_manibus_cons)

EDH$form_dis_manibus <- ifelse(grepl("d m s", EDH$form_dis_manibus, ignore.case = T),  "dis manibus sacrum", 
                               ifelse(grepl("d m", EDH$form_dis_manibus, ignore.case = T),  "dis manibus",
                               EDH$form_dis_manibus))

table(EDH$form_dis_manibus)

## 
##            di manes           dii manes        diis manibus diis manibus sacrum 
##                   2                   1                  75                  17 
##         dis manibus  dis manibus sacrum 
##                8712                3112

# overview
table(str_extract(EDH$clean_text_interpretive_word_lowercase, dis_manibus_regex_minimal))

## 
##        diis manibus diis manibus sacrum         dis manibus  dis manibus sacrum 
##                  75                  17                8549                3102

table(str_extract(EDH$clean_text_conservative_lowercase, dis_manibus_regex_maximal))

## 
##                 d m               d m s            di manes            di manis 
##                6671                2531                   2                   1 
##           dii manes        diis manibus diis manibus sacrum         dis manibus 
##                   1                  54                   7                 250 
##  dis manibus sacrum 
##                  20

# capturing context - 5 words before the formula and 5 words after the formula
#EDH<- EDH %>% 
#  mutate(dis_manibus_in_context_10w = str_extract(EDH$clean_text_interpretive_word_lowercase, "(\\w*\\s){0,6}di{1,2}s manibus sacrum|di{1,2}s manibus(\\w*\\s){0,6}"))

Subsetting dataset to contain only records with formula

#non-empty dis manibus attribute
EDH_DM <- EDH %>% 
  filter(!is.na(form_dis_manibus))

Save the dataset locally

EDH_DMjson <- toJSON(EDH_DM, simplifyVector = TRUE)
write(EDH_DMjson, "../data/EDH_DM.json")

3 Exploration of Dis Manibus inscriptions

3.1 Formulae variants

3.1.1 Ratio of inscriptions with DM formula

nrow(EDH %>% 
  filter(!is.na(form_dis_manibus)))/(nrow(EDH)/100)

## [1] 14.62885

3.1.2 Type of all inscription with DM formula

EDH_DM %>% 
  count(type_of_inscription_clean, sort = T) %>% 
  mutate(ratio = round(n/(sum(n)/100),2))

3.1.3 Ratio of funerary inscriptions with DM formula

epitaph_all<- length(EDH$type_of_inscription_clean[EDH$type_of_inscription_clean == "epitaph"])
epitaph_dm<- length(EDH_DM$type_of_inscription_clean[EDH_DM$type_of_inscription_clean == "epitaph"])

epitaph_dm/(epitaph_all/100)

## [1] 33.61357

3.1.4 Type of the formula overall

form_dm<- EDH_DM %>% 
  #filter(type_of_inscription_clean %in% c("epitaph", "NULL")) %>% 
  filter(!is.na(form_dis_manibus)) %>% 
  count(form_dis_manibus, sort=T) %>% 
  mutate(ratio = round(n/(sum(n)/100),2))

form_dm

write_csv(form_dm, "../output/DM_form_total_numbers.csv")

3.2 Province

3.2.1 Type of formula by province - frequence

EDM_DM_freq<- EDH_DM %>% 
  #count(province_label_clean, form_dis_manibus, sort=T) %>% 
  ggplot(aes(y=province_label_clean)) + 
  geom_bar(aes(fill=form_dis_manibus)) +
  #coord_cartesian(xlim=c(0,1700)) +
  theme_minimal() +
  theme(text = element_text(size=12)) +
  labs(y="Roman province", x="Formula", title= "Frequence of Dis Manibus formula by province", subtitle = ggtitle(paste("n =", nrow(EDH_DM), "inscriptions (EDH)")))  
ggsave("../figures/EDM_DM_province_freq.png")

## Saving 14 x 10 in image

EDM_DM_freq

3.2.2 Ratio of DM variants per province

DM_form<- EDH_DM %>% 
  select(form_dis_manibus, province_label_clean) %>% 
  count(form_dis_manibus, province_label_clean, sort=T)


EDH_DM %>% 
  ggplot(aes(y=province_label_clean, fill=form_dis_manibus)) +
  geom_bar(position="fill") +
  theme_minimal() +
  theme(text = element_text(size=12)) +
  geom_vline(xintercept=c(0.25, 0.5, 0.75), linetype="dotted", color = "blue", size=0.8) + 
  labs(y="Roman province", x="Formula", title= "Ratio of Dis Manibus formulae by province", subtitle = ggtitle(paste("n =", nrow(EDH_DM), "inscriptions (EDH)")))

  #geom_label(aes(label= form_dis_manibus)) +
  #geom_label(aes(label = form_dis_manibus), colour = "black", fontface = "bold", hjust = -0.1)

3.2.3 Provinces with Dis manibus sacrum

EDH_DM$province_label_clean <-factor(EDH_DM$province_label_clean)

EDH_DM$form_dis_manibus <-factor(EDH_DM$form_dis_manibus)


EDH_DM %>% 
  count(form_dis_manibus, province_label_clean, sort=T) %>% 
  group_by(form_dis_manibus) %>%  
  filter(form_dis_manibus == "dis manibus sacrum") %>% 
  ggplot(aes(y=fct_reorder(province_label_clean, n), x=n)) +
  geom_col(fill= "blue") +
  labs(y="Roman province", x="n", title= "Frequence of 'Dis Manibus Sacrum' formula by province", subtitle = ggtitle(paste("n =", nrow(EDH_DM), "inscriptions (EDH)"))) +
  geom_text(aes(label = n), colour = "black", size= 3, hjust = -0.1)

EDH_DM %>% 
  count(form_dis_manibus, province_label_clean, sort=T) %>% 
  group_by(form_dis_manibus) %>%  
  filter(form_dis_manibus == "dis manibus") %>% 
  ggplot(aes(y=fct_reorder(province_label_clean, n), x=n)) +
  geom_col(fill= "blue") +
  labs(y="Roman province", x="n", title= "Frequence of 'Dis Manibus' formula by province", subtitle = ggtitle(paste("n =", nrow(EDH_DM), "inscriptions (EDH)"))) +
  geom_text(aes(label = n), colour = "black", size= 3, hjust = -0.1)

3.2.4 Ratio of formulae per province normalized by the number of total inscriptions

DM_form_province<- EDH_DM %>% 
  count(form_dis_manibus, province_label_clean, sort=T) %>% 
  pivot_wider(names_from = form_dis_manibus, values_from = n)

province_total_insc<- EDH %>% 
  count(province_label_clean)

provinces_DM<- merge(x=province_total_insc, y=DM_form_province, by="province_label_clean", all=T)

provinces_DM<- provinces_DM %>% 
  rename(all_insc=n)

provinces_DM <- provinces_DM %>% 
  mutate(dm = provinces_DM$`dis manibus`+provinces_DM$`diis manibus`) %>% 
  mutate(dms = provinces_DM$`dis manibus sacrum`+provinces_DM$`diis manibus sacrum`) %>% 
  mutate(dm_ratio = round(dm/(all_insc/100),2)) %>% 
  mutate(dms_ratio = round(dms/(all_insc/100),2))

provinces_DM

write_csv(provinces_DM, "../output/DM_form_provinces.csv")

3.2.5 How common the dm and the dms formulae are (of all inscriptions from a given province)

#Filter those provinces where the ratio is higher than mean for all provinces fro a given formula

higher_dm<- provinces_DM %>% 
    filter(dm_ratio > mean(na.omit(provinces_DM$dm_ratio)))
higher_dm

higher_dms<- provinces_DM %>% 
    filter(dms_ratio > mean(na.omit(provinces_DM$dms_ratio)))
higher_dms

higher_dm_dms<- provinces_DM %>% 
    filter(dm_ratio > mean(na.omit(provinces_DM$dm_ratio)& dms_ratio > mean(na.omit(provinces_DM$dms_ratio))))

## Warning in na.omit(provinces_DM$dm_ratio) & dms_ratio >
## mean(na.omit(provinces_DM$dms_ratio)): longer object length is not a multiple of
## shorter object length

higher_dm_dms

3.3 Mapping

3.3.1 Map of Dis Manibus occurences - cummulative

# preparation of subsets
# dis manibus formula
EDH_dis_manibus <- EDH_DM %>% 
  filter(form_dis_manibus == "dis manibus")

# diis manibus formula
EDH_diis_manibus <- EDH_DM %>% 
  filter(form_dis_manibus == "diis manibus")

# dis manibus sacrum formula
EDH_dis_manibus_sacrum <- EDH_DM %>% 
  filter(form_dis_manibus == "dis manibus sacrum")

# diis manibus sacrum formula
EDH_diis_manibus_sacrum <- EDH_DM %>% 
  filter(form_dis_manibus == "diis manibus sacrum")

DM_map<- #head(EDH_DM, 100) %>% 
  leaflet(EDH_DM) %>% 
  leaflet(width="100%") %>%
  #addProviderTiles("Stamen.Watercolor")%>% # Add CartoDB map tiles
  addProviderTiles("Stamen.TerrainBackground")%>% # Add CartoDB map tiles
  #addProviderTiles("Esri.WorldTopoMap", group = "Topo") %>%
  #addProviderTiles("Esri.WorldImagery", group = "ESRI Aerial") %>%
  setView( lng = 12.9239625, lat = 41.9515694, zoom = 3.5 ) %>%
  #setMaxBounds(lat1=43.633977, lng1 =-11.227926 , lat2=35.133882 , lng2=50.882336) %>%
  #addPolylines(data = roads, color = "purple", weight = 1, opacity = 0.7) %>% 
  
  addCircles(lng = EDH_dis_manibus$longitude, 
             lat = EDH_dis_manibus$latitude, opacity = 0.3, radius = 0.7, fill = T , color = "blue" , fillColor = "blue",) %>%
  addCircles(lng = EDH_dis_manibus_sacrum$longitude, 
             lat = EDH_dis_manibus_sacrum$latitude, opacity = 0.3, radius = 0.7, fill = T , color = "red" , fillColor = "red",) %>%
    addCircles(lng = EDH_diis_manibus$longitude, 
             lat = EDH_diis_manibus$latitude, opacity = 0.3, radius = 0.7, fill = T , color = "black" , fillColor = "black",) %>%
  addCircles(lng = EDH_diis_manibus_sacrum$longitude, 
             lat = EDH_diis_manibus_sacrum$latitude, opacity = 0.3, radius = 0.7, fill = T , color = "orange" , fillColor = "orange",) %>%
  

  #addAwesomeMarkers(~EDH$longitude, ~EDH$latitude, icon=icons) %>% 
  addLegend(position = "bottomright",
  colors = c("Blue", "Green", "Red", "Orange"),
  labels = c("dis manibus", "diis manibus", "dis manibus sacrum", "diis manibus sacrum"), opacity = 1,
  title = "Dis Manibus formula (EDH)" 
) %>% 
  addScaleBar(position="bottomleft")

## Warning in validateCoords(lng, lat, funcName): Data contains 2 rows with either
## missing or invalid lat/lon values and will be ignored

## Warning in validateCoords(lng, lat, funcName): Data contains 4 rows with either
## missing or invalid lat/lon values and will be ignored

## Warning in validateCoords(lng, lat, funcName): Data contains 185 rows with
## either missing or invalid lat/lon values and will be ignored

## Warning in validateCoords(lng, lat, funcName): Data contains 195 rows with
## either missing or invalid lat/lon values and will be ignored

DM_map

3.4 Demographic preferences

Preparation of the dataset - unique people records

EDH_small <- EDH_DM %>% 
  select(id, people, form_dis_manibus, type_of_inscription_clean, type_of_monument_clean)

EDH_smu <- EDH_small %>% unnest_wider(people)
names(EDH_smu)

##  [1] "id"                        "name"                     
##  [3] "cognomen"                  "nomen"                    
##  [5] "person_id"                 "gender"                   
##  [7] "praenomen"                 "age: years"               
##  [9] "status"                    "occupation"               
## [11] "age: days"                 "age: months"              
## [13] "origo"                     "tribus"                   
## [15] "age: hours"                "supernomen"               
## [17] "form_dis_manibus"          "type_of_inscription_clean"
## [19] "type_of_monument_clean"

# this works only on Linux computer, not on Windows

EDH_people<- EDH_small %>% unnest_wider(people) %>% 
  unnest_longer(names(EDH_smu))

3.4.1 Gender of person on inscription

EDH_people<- EDH_people %>% 
  #filter(form_dis_manibus == "dis manibus" | form_dis_manibus == "dis manibus sacrum") %>% 
  mutate(gender_clean = str_replace_all(gender, "W\\?", "female")) %>%
  mutate(gender_clean = str_replace_all(gender_clean, "M\\?", "male"))


EDH_people %>% 
  count(gender_clean, form_dis_manibus, sort=T) %>% 
  mutate(freq = n/(sum(n)/100)) %>% 
  ggplot(aes(y=gender_clean, x=n)) +
  geom_col(aes(fill=form_dis_manibus)) +
  #geom_label(aes(label = round(freq, 2)), hjust=0.3, size = 3.5) +
  theme_linedraw(base_size = 12) +
  labs(y="Gender", x="Formula", title= "Dis Manibus formula by gender", subtitle = ggtitle(paste("n =", length(EDH_DM$people), "inscriptions (EDH)")))

EDH_people %>% 
  ggplot(aes(x=gender_clean, fill=form_dis_manibus)) +
  geom_bar(position="fill") +
  #geom_label(aes(label = round(freq, 2)), hjust=0.3, size = 3.5) +
  #theme_linedraw(base_size = 12) +
  labs(y="Gender", x="Formula", title= "Ratio of Dis Manibus formula by gender", subtitle = ggtitle(paste("n =", length(EDH_DM$people), "inscriptions (EDH)")))

3.4.2 Status of person on inscription

EDH_people %>% 
  #filter(form_dis_manibus == "dis manibus" | form_dis_manibus == "dis manibus sacrum") %>% 
  mutate(status_clean = str_replace_all(status, "\\?", "")) %>% 
  count(status_clean, form_dis_manibus, sort=T) %>% 
  ggplot(aes(y=status_clean, x=n)) +
  geom_col(aes(fill=form_dis_manibus)) +
  #coord_cartesian(xlim = c(0, 15800)) +
  labs(y="Status", x="Individuals", title= "Dis Manibus formula by personal status", subtitle = ggtitle(paste("n =", nrow(EDH_people), "individuals (EDH)"))) +
  theme(legend.position = c(0.9, 0.15)) +
  scale_fill_discrete(name="Formula")

EDH_people<- EDH_people %>% 
  #filter(form_dis_manibus == "dis manibus" | form_dis_manibus == "dis manibus sacrum") %>% 
  mutate(status_clean = str_replace_all(status, "\\?", ""))

Status of a person was split into new column status_highest that records only the highest status category in the following order: 1) slaves, 2) freedmen / freedwomen, 3) Augustales, 4) military personnel, 5) lower local offices, administration of imperial estates, 6) decurial order, high local offices, 7) equestrian order, 8) senatorial order, 9) rulers (foreign)

EDH_people$status_highest <- ifelse(grepl("decurial order, higher local offices; military personnel", EDH_people$status_clean, ignore.case = T),  "decurial order, higher local offices",
                                    ifelse(grepl("equestrian order; decurial order, higher local offices", EDH_people$status_clean, ignore.case = T), "equestrian order",
                                           ifelse(grepl("senatorial order; equestrian order", EDH_people$status_clean, ignore.case = T), "senatorial order", 
                                                  ifelse(grepl("Augustales; freedmen / freedwomen", EDH_people$status_clean, ignore.case = T), "Augustales", 
                                                         ifelse(grepl("decurial order, higher local offices; Augustales", EDH_people$status_clean, ignore.case = T), "decurial order, higher local offices", 
                                                                ifelse(grepl("decurial order, higher local offices; freedmen / freedwomen", EDH_people$status_clean, ignore.case = T), "decurial order, higher local offices",
                                                                       ifelse(grepl("equestrian order; lower local offices, administration of imperial estates", EDH_people$status_clean, ignore.case = T), "equestrian order",
                                                                          
                                                  EDH_people$status_clean)))))))

EDH_status_count <- EDH_people %>% 
  count(form_dis_manibus, status_clean, sort = T) %>% 
  mutate(ratio = round(n/(sum(n)/100),2)) 

mean_dms <- mean(EDH_status_count$ratio[EDH_status_count$form_dis_manibus == "dis manibus sacrum"])

EDH_people %>% 
  ggplot(aes(y=status_highest, fill=form_dis_manibus)) +
  geom_bar(position="fill") +
  #coord_cartesian(xlim = c(0, 15800)) +
  labs(y="Status", x="Individuals", title= "Ratio of Dis Manibus formula by personal status", subtitle = ggtitle(paste("n =", nrow(EDH_people), "individuals (EDH)"))) +
  #theme(legend.position = c(0.9, 0.15)) +
  scale_fill_discrete(name="Formula") +
  geom_vline(xintercept =mean_dms/10, linetype = "longdash", size=0.3)

3.4.3 Age of person on inscription

EDH_people %>% 
  mutate(age_yr = as.numeric(`age: years`)) %>% 
  filter(!is.na(age_yr)) %>% 
  count(age_yr, form_dis_manibus, sort=T) %>% 
  ggplot(aes(age_yr, n, colour= form_dis_manibus)) +
  geom_point(aes(colour=form_dis_manibus)) +
  labs(y="Individuals", x="Years at death", title= "Dis Manibus formula by age in years of the deceased", subtitle = ggtitle(paste("n =", nrow(EDH_people), "individuals (EDH)")))+
  geom_smooth(se = FALSE, method = lm) +
  facet_wrap(~form_dis_manibus)

## Warning in mask$eval_all_mutate(quo): NAs introduced by coercion

## `geom_smooth()` using formula 'y ~ x'

3.4.3.1 Average age per formula type

@continue here

average_age <- EDH_people %>% 
  mutate(age_yr = as.numeric(`age: years`)) %>% 
  filter(!is.na(age_yr)) %>%
  select(age_yr)

## Warning in mask$eval_all_mutate(quo): NAs introduced by coercion

average_age_all<- mean(na.omit(average_age$age_yr))

average_age_per_formula <- EDH_people %>% 
  mutate(age_yr = as.numeric(`age: years`)) %>% 
  filter(!is.na(age_yr)) %>%
  group_by(form_dis_manibus) %>% 
  summarise_at(vars(age_yr), list(name=mean)) %>% 
  mutate(diff_av_age  =  name-average_age_all)

## Warning in mask$eval_all_mutate(quo): NAs introduced by coercion

average_age_per_formula

Saving DM with people data locally:

EDH_DMpeoplejson <- toJSON(EDH_people, simplifyVector = TRUE)
write(EDH_DMpeoplejson, "../data/EDH_DM_people.json")

3.4.4 Inscribed monument

EDH_DM %>% 
  count(type_of_monument_clean, form_dis_manibus, sort = T) %>% 
  ggplot(aes(y=type_of_monument_clean, x=n)) + 
  geom_col(aes(fill=form_dis_manibus)) +
  theme_minimal() +
  labs(y="Inscribed object", x="Inscriptions", title= "Dis Manibus formula by inscribed object", subtitle = ggtitle(paste("n =", nrow(EDH_DM), "inscriptions (EDH)")))

3.4.5 Inscribed material

EDH_DM %>% 
  count(material_clean, form_dis_manibus, sort = T) %>% 
  ggplot(aes(y=material_clean, x=n)) + 
  geom_col(aes(fill=form_dis_manibus)) +
  theme_minimal() +
  labs(y="Inscribed material", x="Inscriptions", title= "Dis Manibus formula by inscribed material", subtitle = ggtitle(paste("n =", nrow(EDH_DM), "inscriptions (EDH)")))

Aarhus University, Denmark, https://orcid.org/0000-0002-6349-0540 ↩︎

Exploration of the epigraphic formula `Dis manibus` on Latin inscriptions

Petra Hermankova¹

2022-04-24

1 Setup

1.1 Text preparation

1.2 Preparation coordinates for mapping

2 Dis Manibus epigraphic formula in space and time

2.1 Extracting inscriptions with for all typical variants of the formula

3 Exploration of Dis Manibus inscriptions

3.1 Formulae variants

3.1.1 Ratio of inscriptions with DM formula

3.1.2 Type of all inscription with DM formula

3.1.3 Ratio of funerary inscriptions with DM formula

3.1.4 Type of the formula overall

3.2 Province

3.2.1 Type of formula by province - frequence

3.2.2 Ratio of DM variants per province

3.2.3 Provinces with Dis manibus sacrum

3.2.4 Ratio of formulae per province normalized by the number of total inscriptions

3.2.5 How common the dm and the dms formulae are (of all inscriptions from a given province)

3.3 Mapping

3.3.1 Map of Dis Manibus occurences - cummulative

3.4 Demographic preferences

3.4.1 Gender of person on inscription

3.4.2 Status of person on inscription

3.4.3 Age of person on inscription

3.4.3.1 Average age per formula type

3.4.4 Inscribed monument

3.4.5 Inscribed material

Exploration of the epigraphic formula Dis manibus on Latin inscriptions

Petra Hermankova1

2022-04-24

1 Setup

1.1 Text preparation

1.2 Preparation coordinates for mapping

2 Dis Manibus epigraphic formula in space and time

2.1 Extracting inscriptions with for all typical variants of the formula

3 Exploration of Dis Manibus inscriptions

3.1 Formulae variants

3.1.1 Ratio of inscriptions with DM formula

3.1.2 Type of all inscription with DM formula

3.1.3 Ratio of funerary inscriptions with DM formula

3.1.4 Type of the formula overall

3.2 Province

3.2.1 Type of formula by province - frequence

3.2.2 Ratio of DM variants per province

3.2.3 Provinces with Dis manibus sacrum

3.2.4 Ratio of formulae per province normalized by the number of total inscriptions

3.2.5 How common the dm and the dms formulae are (of all inscriptions from a given province)

3.3 Mapping

3.3.1 Map of Dis Manibus occurences - cummulative

3.4 Demographic preferences

3.4.1 Gender of person on inscription

3.4.2 Status of person on inscription

3.4.3 Age of person on inscription

3.4.3.1 Average age per formula type

3.4.4 Inscribed monument

3.4.5 Inscribed material

Exploration of the epigraphic formula `Dis manibus` on Latin inscriptions

Petra Hermankova¹